Search Images Maps Play YouTube News Gmail Drive More »
Sign in
Screen reader users: click this link for accessible mode. Accessible mode has the same essential features but works better with your reader.

Patents

  1. Advanced Patent Search
Publication numberUS8219518 B2
Publication typeGrant
Application numberUS 11/621,521
Publication dateJul 10, 2012
Filing dateJan 9, 2007
Priority dateJan 9, 2007
Also published asUS20080168082, US20120271865
Publication number11621521, 621521, US 8219518 B2, US 8219518B2, US-B2-8219518, US8219518 B2, US8219518B2
InventorsQi Jin, Hui Liao, Sriram Srinivasan, Lin Xu
Original AssigneeInternational Business Machines Corporation
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Method and apparatus for modelling data exchange in a data flow of an extract, transform, and load (ETL) process
US 8219518 B2
Abstract
Methods, systems, and computer program products for generating code from a data flow associated with an extract, transform, and load (ETL) process. In one implementation, the method includes identifying a data exchange requirement between a first operator and a second operator in the data flow. The first operator is a graphical object that represents a first data transformation step in the data flow and is associated with a first type of runtime engine, and the second operator is a graphical object that represents a second data transformation step in the date flow and is associated with a second type of runtime engine. The method further includes generating code to manage data staging between the first operator and the second operator in the data flow. The code exchanges data from a format associated with the first type of runtime engine to a format associated with the second type of runtime engine.
Images(8)
Previous page
Next page
Claims(20)
1. A computer-implemented method comprising:
receiving a data flow defining a sequence of operations for an associated extract, transform, and load (ETL) process;
converting the data flow into a logical operator graph (LOG), the logical operator graph being a representation of the data flow and including a plurality of operators corresponding to the sequence of operations defined by the data flow;
converting the logical operator graph into a query graph model (QGM) including inserting, based on pre-determined criteria, a data station operator between a first operator and a second operator of the plurality of operators associated with the logical operator graph, wherein the first operator is associated with a first type of runtime engine, wherein the second operator is associated with a second type of runtime engine distinct from the first type, the data station operator representing a staging point operable to exchange data from a format associated with the first type of runtime engine to a format associated with the second type of runtime engine; and
generating an execution plan graph based on the query graph model, the execution plan graph including code to manage data staging between the first operator and the second operator.
2. The method of claim 1, wherein inserting the data station based on pre-determined criteria includes inserting the data station based on criteria associated with optimization, error recovery and restart, diagnostics and debugging, or cross-system data exchanges.
3. The method of claim 1, further comprising receiving user input overriding any automatic decisions to insert a data station based on the pre-determined criteria.
4. The method of claim 1, wherein inserting the data station based on pre-determined criteria includes inserting the data station responsive to user input explicitly specifying the data station between the first operator and the second operator.
5. The method of claim 1, wherein inserting the data station based on pre-determined criteria includes inserting a corresponding data station for each of the plurality of operators that requires chunking.
6. The method of claim 1, wherein a data exchange requirement is identified between the first operator and the second operator, wherein the code included in the execution plan graph is configured to exchange data from the format associated with the first type of runtime engine to the format associated with the second type of runtime engine, wherein the data station operator is inserted by a data processing application, and wherein the data processing application is configured to insert the data station operator upon each of:
(i) receiving user input explicitly specifying to insert the data station operator between the first operator and the second operator;
(ii) identifying that at least one of the first operator and the second operator requires chunking, wherein the identified operator is selected from a splitter operator, an inner join operator, a key lookup operator, an operator that supports discard of rows, and an operator that requires staging; and
(iii) determining that the first operator is associated with the first type of runtime engine and that the second operator is associated with the second type of runtime engine distinct from the first type.
7. The method of claim 1, wherein inserting the data station based on pre-determined criteria includes inserting a data station between two linked operators of the plurality of operators that are incompatible.
8. The method of claim 7, wherein two linked operators of the plurality of operators are incompatible based on runtime implementation code for each of the two linked operators being different.
9. The method of claim 1, wherein the first type of runtime engine comprises a runtime engine operable to process Structured Query Language (SQL) based operators, and wherein the second type of runtime engine is a runtime engine operable to process non-SQL based operators.
10. The method of claim 1, wherein the first type of runtime engine comprises a relational database server, and wherein the second type of runtime engine comprises an ETL engine.
11. The method of claim 1, wherein the data station operator includes at least one of the following attributes: a data station type that specifies a format of data staged within the staging point, a pass through flag indicating if the data station operator can be ignored, a name indicating a staging table name or a staging file name, a data station lifetime specifying a time when the data station object is removable from the data flow, or a performance hint including pre-determined information to improve performance of execution of the data flow.
12. The method of claim 1, wherein the staging point stores at least one of a temporary database table, a permanent database table, a database view, a Java Database Connectivity (JDBC) result set, and a flat file.
13. A computer program product tangibly stored on a computer-readable storage medium, the computer program product comprising instructions for causing a programmable processor to:
receive a data flow defining a sequence of operations for an associated extract, transform, and load (ETL) process;
convert the data flow into a logical operator graph (LOG), the logical operator graph being a representation of the data flow and including a plurality of operators corresponding to the sequence of operations defined by the data flow;
convert the logical operator graph into a query graph model (QGM) including inserting, based on pre-determined criteria, a data station operator between a first operator and a second operator of the plurality of operators associated with the logical operator graph, wherein the first operator is associated with a first type of runtime engine, wherein the second operator is associated with a second type of runtime engine distinct from the first type, the data station operator representing a staging point operable to exchange data from a format associated with the first type of runtime engine to a format associated with the second type of runtime engine; and
generate an execution plan graph based on the query graph model, the execution plan graph including code to manage data staging between the first operator and the second operator.
14. The computer program product of claim 13, wherein the instructions to insert the data station based on pre-determined criteria include instructions to insert the data station based on criteria associated with optimization, error recovery and restart, diagnostics and debugging, or cross-system data exchanges.
15. The computer program product of claim 13, wherein the instructions to insert the data station based on pre-determined criteria include instructions to insert the data station responsive to user input explicitly specifying the data station between the first operator and the second operator.
16. The computer program product of claim 13, wherein the instructions to insert the data station based on pre-determined criteria include instructions to insert a corresponding data station for each of the plurality of operators that requires chunking.
17. The computer program product of claim 13, wherein the first type of runtime engine comprises a runtime engine operable to process Structured Query Language (SQL) based operators, and wherein the second type of runtime engine is a runtime engine operable to process non-SQL based operators.
18. The computer program product of claim 13, wherein the first type of runtime engine comprises a relational database server, and wherein the second type of runtime engine comprises an ETL engine.
19. The computer program product of claim 13, wherein the data station operator includes at least one of the following attributes: a data station type that specifies a format of data staged within the staging point, a pass through flag indicating if the data station operator can be ignored, a name indicating a staging table name or a staging file name, a data station lifetime specifying a time when the data station object is removable from the data flow, or a performance hint including pre-determined information to improve performance of execution of the data flow.
20. The computer program product of claim 13, wherein the staging point stores at least one of a temporary database table, a permanent database table, a database view, a Java Database Connectivity (JDBC) result set, and a flat file.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Patent Application entitled “System and Method for Generating Code for an Integrated Data System,” Ser. No. 11/372,540, filed on Mar. 10, 2006, U.S. Patent Application entitled “Data Flow System and Method for Heterogeneous Data Integration Environments,” Ser. No. 11/373,685, filed on Mar. 10, 2006, U.S. Patent Application entitled “Dilation of Sub-Flow Operators in a Data Flow,” Ser. No. 11/372,516, filed on Mar. 10, 2006, U.S. Patent Application entitled “Classification and Sequencing of Mixed Data Flows,” Ser. No. 11/373,084, filed on Mar. 10, 2006, U.S. Patent Application entitled “Method and Apparatus for Managing Application Parameters,” Ser. No. 11/548,632, filed on Oct. 11, 2006, U.S. Patent Application entitled “Method and Apparatus for Generating Code for an Extract, Transform, and Load (ETL) Data Flow,” Ser. No. 11/548,659, filed on Oct. 11, 2006, and U.S. Patent Application entitled “Method and Apparatus for Using Set Based Structured Query Language (SQL) to Implement Extract, Transform, and Load (ETL) Splitter Operation,” Ser. No. 11/610,480, filed on Dec. 13, 2006 the disclosures of which are incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to data processing, and more particularly to modeling data exchange in a data flow associated with an extract, transform, and load (ETL) process.

BACKGROUND OF THE INVENTION

Extract, transform, and load (ETL) is a process in data warehousing that involves extracting data from outside sources, transforming the data in accordance with particular business needs, and loading the data into a data warehouse. An ETL process typically begins with a user defining a data flow that defines data transformation activities that extract data from, e.g., flat files or relational tables, transform the data, and load the data into a data warehouse, data mart, or staging table. A data flow, therefore, typically includes a sequence of operations modeled as data flowing from various types of sources, through various transformations, and finally ending in one or more targets, as described in U.S. patent application entitled “Classification and Sequencing of Mixed Data Flows” incorporated by reference above. In the course of execution of a data flow, data sometimes needs to be exchanged or staged at intermediate points within the data flow. The staging of data typically includes saving the data temporarily either in a structured physical storage medium (such as in a simple file) or in database temporary tables or persistent tables. In some cases, it may be optimal to save rows of data in the processing program's memory itself, especially when large and fast caches are present in the system (such “staging” is often referred to as “caching”).

ETL vendors conventionally support data exchange and staging internally inside of an ETL engine in a proprietary fashion, especially if the ETL engine is running outside of a relational database. For example, the DataStage ETL engine permits users to build “stages” of operations—i.e., discrete steps in the transformation sequence—and physically move rows between different stage components in memory. (Note: The term “stage” as used in the context of the DataStage engine—does not refer to the concept of saving rows to a physical media, but rather to unique operational steps). This method, typically allows for some types of performance optimizations; however, the rows of data being moved between the different stages are usually in an internal format (stored in internal memory formats in buffer pools) and the only way a user can view the rows of data is to explicitly define a File Target (or a Table Target) in the data flow and force the rows of data to be saved into a file (or a table)—i.e., only the target of such a data flow can physically export the rows into a user recognizable format.

Accordingly, a common problem of conventional data exchange and staging techniques is that users are not able to specify staging points explicitly and directly in the middle of a data flow, but only as the end of a transformation sequence using target operators. Target operators typically do not serve as an exchange operator—since target operators are destinations. For example, if a user needs to extract rows from a SQL (structured query language) table and pass the rows as input to another type of system which requires a file as input, then the user would have to represent such a process with a first job—as a Table Source operation followed by a File Target or Export operation having a specific file name. The user would then have to schedule a second (separate) job to invoke an operation that uses the file as input.

BRIEF SUMMARY OF THE INVENTION

In general, this specification describes methods, systems, and computer program products for generating code from a data flow associated with an extract, transform, and load (ETL) process. In one implementation, the method includes identifying a data exchange requirement between a first operator and a second operator in the data flow. The first operator is a graphical object that represents a first data transformation step in the data flow and is associated with a first type of runtime engine, and the second operator is a graphical object that represents a second data transformation step in the data flow and is associated with a second type of runtime engine. The method further includes generating code to manage data staging between the first operator and the second operator in the data flow associated with the ETL process. The code exchanges data from a format associated with the first type of runtime engine to a format associated with the second type of runtime engine.

Particular implementations can include one or more of the following advantages. In one aspect, a data station operator is provided that can be inserted into a data flow of an ETL process, in which the data station operator represents a staging point in a data flow. The staging is done to store intermediate processed data for the purpose of tracking, debugging, ease of data recovery, and optimization purposes. In one implementation, the data station operator also permits data exchange between two linked operators that are incompatible in a same single job. Relative to conventional techniques that requires two separate jobs to perform a data exchange between two operators that are incompatible, it is more optimal to use one single job that encompasses both systems, especially if the job is run in parallel and in batches—e.g., if upstream producers and downstream consumers work in sync in a parallel and batch driven mode, the end performance is better.

The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an extract, transform, and load (ETL) system in accordance with one implementation of the invention.

FIG. 2 is a block diagram of a data processing system including a code generation system in accordance with one implementation of the invention.

FIG. 3 is a block diagram of a data flow in accordance with one implementation of the invention.

FIG. 4 is a flow diagram of a method for inserting a data station operator into a data flow in accordance with one implementation of the invention.

FIG. 5 is a flow diagram of method for processing a data flow in accordance with one implementation of the invention.

FIG. 6 illustrates an example data flow including operators associated with two different runtime engines in accordance with one implementation of the invention.

FIG. 5 illustrates an example logical operator graph mapped to a query graph model (QGM) in accordance with one implementation of the invention.

FIG. 6 illustrates an example logical operator graph including operators associated with two different types of runtime engines in accordance with one implementation of the invention.

FIG. 7 illustrates an example query graph model (QGM) graph chunked into several corresponding sub-graphs in accordance with one implementation of the invention.

FIG. 8 is a block diagram of a data processing system suitable for storing and/or executing program code in accordance with one implementation of the invention.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates generally to data processing, and more particularly to modeling data-exchange in a data flow associated with an extract, transform, and load (ETL) process. The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. The present invention is not intended to be limited to the implementations shown but is to be accorded the widest scope consistent with the principles and features described herein.

FIG. 1 illustrates an extract, transform, and load (ETL) system 100 according to one implementation. The ETL system 100 includes a database server 102 that acts as an ETL engine to integrate data (e.g., from data sources A, B, . . . N) through an extract phase, a transform phase, and a load phase. The extract phase includes extracting data from source systems (e.g., from data sources A, B, . . . N). Most data warehousing projects consolidate data from different source systems. Each separate source system may also use a different data organization/format. Common data source formats include, for example, relational databases and flat tiles, and include non-relational database structures such as IMS. The extract phase includes converting the data into a format for transformation processing. The transform phase applies a series of rules or functions to the extracted data to derive the data to be loaded. The load phase loads the data into a data warehouse (e.g., data warehouse 104). Data integration typically begins with a user describing a data flow of an ETL process using a UI (user interface) tool. A data flow represents a logical transformation and flow of data. A code generation system generates code from the data flow, which generated code is then sent to the database server 102 for execution.

FIG. 2 illustrates a data processing system 200 in accordance with one implementation of the invention. The data processing system 200 can comprise the IBM DB2 Data Warehouse Edition (DWE) product available from International Business Machines Corporation of Armonk, N.Y. Data processing system 200 includes input and output devices 202, a programmed computer 204, and a storage device 206. Input and output devices 202 can include devices such as a printer, a keyboard, a mouse, a digitizing pen, a display, a printer, and the like. Programmed computer 204 can be any type of computer system, including for example, a workstation, a desktop computer, a laptop computer, a personal digital assistant (PDA), a cell phone, a network, and so on.

Running on the programmed computer 204 is an integrated development environment 208. The integrated development environment 208 is a software component that assists users (e.g., computer programmers) in developing, creating, editing, and managing code for target platforms.

In one implementation, the integrated development environment 208 includes code generation system 210 that (in one implementation) is operable to generate code to manage data exchange and data staging within a sequence of operations defined in a data flow of an ETL process, as discussed in greater detail below. In one implementation, the code generator 210 generates code using techniques as described in U.S. Patent Application entitled “Classification and Sequencing of Mixed Data Flows,” Ser. No. 11/372,540, filed on Mar. 10, 2006 (the '540 application), which is incorporated by reference above.

In operation, a data flow 212 (e.g., an ETL data flow) is received by the code generation system 210, and the data flow 212 is converted by the code generation system into a logical operator graph (LOG) 214. The logical operator graph 214 is a normalized, minimalist representation of the data flow 212 that includes logical abstract collection of operators (including, e.g., one or more of a splitter operator, join operator, filter operator, table extract operator, bulk load operator, aggregate operator, and so on). In some implementations, all of the contents of the data flow 212 may be used “as-is” by the code generation system 210 and, therefore, the logical operator graph 214 will be the same as the data flow 212. The code generation system 210 converts the logical operator graph 214 into a query graph model (QGM graph) 216. The QGM graph 216 is an internal data model used by the code generation system 210 for analysis and optimization processes, such as chunking (in which a subset of a data flow is broken into several pieces to improve performance) and execution parallelism (in which disparate sets of operations within a data flow are grouped and executed in parallel to yield better performance). After analysis, the QGM 216 is converted into an extended plan graph 218. The extended plan graph 218 represents code generated by the code generation system 210 and is sent to a runtime engine (e.g., an ETL engine) for execution.

In one implementation, the integrated development environment 208 includes a data flow graphical editor (not shown) that enables users to build data flows (e.g., data flow 212). In one implementation, the data flow graphical editor provides a new operator—i.e., a data station operator—that a user can directly drag and drop into a data flow to link a preceding (“upstream”) operator and one or more subsequent (“downstream”) operators, which data station operator specifies a data staging point in the data flow. In general, operators are represented in a data flow as graphical objects. In one implementation, the data station operator can be used as a link between a first operator (or operation) associated with first runtime engine (e.g., a relational database management system) and a second operator associated with a second runtime engine (e.g., a DataStage ETL engine). FIG. 3 illustrates the data flow 212 including a data station operator 300 linking an upstream operator 302 to a downstream operator 304. In one implementation, staging refers to writing data to a disk, and permits a user to store intermediate data at various points of a data flow in a relational table, flat file or a view. A staging point indicates a location on the data flow where the data is staged. In one implementation, a staging point occurs on an operator's output port.

In one implementation, the code generation system 210 is operable to automatically place individual data station operators (into a sequence of operations defined by a data flow) whenever a data exchange requirement is identified during the code generation process. In one implementation, the identification and insertion of the data exchange/staging points are seamless to the end user. Accordingly, in such an implementation, the code generation system 210 is operable to automatically generate code that manages data staging and data exchange on an ETL system that is capable of integrating various data processing runtime engines. For example, if a particular runtime engine can work with flat files as well as database tables, depending on certain optimization considerations, there may not be an exchange necessary, or if flat files are determined to be processed faster, then a file staging from an upstream operation (e.g., associated with a relational database engine) may be decided by the code generation system 210 to be more appropriate—or a decision could be made based on current system loads. A dynamic decision (based on various cost-benefit analyses) on whether a data station operator is required, may be best decided by the code generation system 210. In such cases, any suitable cost-benefit criteria can be implemented. In some cases, however, (expert) users or database administrators may have better knowledge than the code generation system 210 because of an understanding of expected data and expected system stress, e.g., when data is range partitioned and the administrator would be aware of which particular database partition nodes would be stressed. In such cases, it may be more appropriate for a user to explicitly override any staging options automatically selected by the code generation system 210 (or for a user to explicitly define a different staging format when the code generation system 210 does not add one by default).

Accordingly, unlike a conventional system in which a user must represent data staging using two or more jobs in order to exchange data from one runtime format (e.g., database table) to another runtime format (e.g., a flat file) in a data flow, the data processing system 200 permits a user to exchange data from one system-format to another in the same single job through the data station operator. Users can, therefore, use such data stations to explicitly identify points of staging or exchange interest, e.g., for diagnostics, for performance improvements, or for overriding any default choices made by the code generation system 210.

FIG. 4 is a flow diagram illustrating a computer-implemented method 400 for inserting a data station operator into a data flow (e.g., data flow 212) in accordance with one implementation of the invention. The method 400 begins with a data processing system (e.g., data processing system 200) receiving user input inserting a first operator (or operation) associated with a first type of runtime engine into a data flow (e.g., a data flow 212) (step 402). There are various types of runtime engines that may be used to process ETL operations—e.g., a relational database engine or a DataStage ETL engine among others. In addition, there are many different types of operators representing corresponding ETL operations (such as structured query language operations and ETL DataStage operations) that can be inserted by a user into a data flow. Some operators associated with a relational database engine include, for example, a table extract operator, a join operator, a de-duplicate operator, a bulk load table operator, a file target operator, and so on. Some operators associated with a DataStage ETL engine include, for example, a file extract operator, a filter operator, and so on.

User input is received inserting a second operator associated with a second type of runtime engine into the data flow (step 404). In one example, the first operator can be associated with a relational database engine and the second operator can be associated with a DataStage ETL engine. The first operator and the second operator can be transform operators that represent data transformation steps in the data flow. User input is received inserting a data station operator (e.g., data station operator 300) into the data flow between the first operator and the second operator to link the first operator and the second operator (step 406). Thus, the data processing system permits the user to explicitly add a data staging operator into a data flow, in which the data staging operator exchanges data from a format associated with the first runtime engine into a format associated with the second runtime engine in a same single job.

FIG. 5 illustrates 5 a method 500 for processing a data flow in accordance with one implementation of the invention. The method 500 begins with a code generation system (e.g., code generation system 210) receiving a data flow defining a sequence of operations for an ETL process (step 502). A data flow represents a logical transformation and flow of data, and is typically built based on user input. For example, versions of the IBM DB2 Data Warehouse Edition (DWE) product have a data flow graphical editor that enables users to build data flows. The data flow can include one or more data stations operators to link corresponding upstream operators and downstream operators within the data flow, as discussed above. The data flow is converted to a logical operator graph (LOG) (e.g., logical operator graph 214) by the code generation system (step 304). As discussed above, (in one implementation) the logical operator graph (LOG) is a minimalist representation of the data flow and includes an abstract collection of operators. The logical operator graph is converted (e.g., by code generation system 210) into a query graph model (QGM) (e.g., QGM graph 216) (step 306). As discussed above, the query graph model is an internal data structure used by the code generation system for analysis and optimization purposes. In one implementation, the code generation system automatically inserts one or more additional data station operators into the sequence of operations of the ETL process based on a cost-benefit analysis. In another implementation, the code generation system automatically inserts one or more data station operators into the sequence of operations of the ETL process based on pre-determined criteria.

Pre-determined criteria upon which the code generation system (or a user) may decide to insert a data station operator into a data flow include, for example, criteria associated with optimization, error recovery and restart, diagnostics and debugging, and cross-system data exchanges. With regard to optimization, intermediate (calculated) data may be staged to avoid having to perform the same calculation multiple times, especially in cases where the output of a single upstream operation is required by multiple downstream operations. Even when there is only one downstream consumer of the output data of a given operation, it may be prudent to stage rows of the output data, especially to a physical storage, in order to either free up memory or avoid stressing an execution system (for example, to avoid running out of database log space). With respect to error recovery and restart, in complex systems, errors during the execution of data flows may occur either due to bad (dirty) data which may cause database inconsistencies, or fatal errors doe to software failures, power loss etc. In many eases, manual intervention is required to bring databases and other systems back to a consistent state. Thus in one implementation, the code generation system (or user) inserts data station operators at specific consistency check points in the data flow, so that staging can be performed on intermediate results in a physical media (for example, in database persistent tables or files). Accordingly, restarts (either manual or automatic) can be performed starting at these check points, thus, saving quite a bit of time.

In terms of diagnostics and debugging, staging may allow administrators to identify the core cause of problems, for example, an administrator can inspect staged rows to find bad data, which may even require the administrator to re-organize ETL processes to first clean such data. Users may also explicitly add data stations in a data flow to aid in debugging of the data flow, e.g., during development and testing cycles. An inspection of such staged rows will provide a validation of whether the corresponding upstream operations did indeed perform as expected. With regard to cross-system exchanges, a data processing system that is capable of integrating various data processing engines such as the one described in the '540 application, the data being processed in such a data processing system is a mix of various data types and formats that are specific to a given underlying (runtime) data processing engine. Some runtime processing systems may be equipped to process data inside database tables, others may only work with flat files, while others may perform better using Message Queues. In some scenarios, external systems in a different (remote) site may be required to complete part of an operation—e.g., a “Name Address Lookup” facility which may be provided by an online vendor for cleansing customer addresses. Such an external vendor may even require a SOAP-based web service means of data movement.

For example, FIG. 6 illustrates an example logical operator graph 600 including a first section 602 and a second section 604. The first section 602 includes operators 606-610 operable to process data having a format compatible with a first type of runtime engine (e.g., a relational database engine), and the second section 604 includes operators 612-620 that are operable to process data having a format compatible with a second type of runtime engine (e.g., a data stage ETL engine). For data that is produced by an operator in the first section 602 to be able to be consumed by an operator in the second section 604, staging objects 622 are generated by a code generation system to exchange data between the first section 602 and the second section 604 of the logical operator graph 600. The code generation system may convert the operators 606-610 into corresponding SQL/PL statements, and convert the operators 606-610 into an extensible mark-up language presentation.

Referring back to FIG. 5, an execution plan graph (e.g., execution plan graph 218) is generated (e.g., by code generation system 210) based on the query graph model (step 308). In one implementation, the execution plan graph includes code (e.g., for generating staging objects) to manage data exchange and data staging corresponding to locations in the sequence of operations of the ETL process in which the user or the code generation system have inserted a data station operator.

Provided below is further discussion regarding implementations of a data station operator and uses thereof.

Model of a Data Station Operator

In one implementation, a data station operator models a data exchange/staging object, and a code generator system generates code that supports staging and data exchange functionalities based on the data station operator. In one implementation, a data station operator is modeled using a data flow operator modeling framework as described in the '540 application. More generally, the concept of an operator is generic to many different ETL or transformation frameworks and, therefore, the concept of a data station operator can be extended for systems other many types of data processing systems. In one implementation, a data station operator has one input port, and one output port, and includes one or more of the following attributes as shown in Table 1 below.

TABLE 1
Attribute Definition
data station type The type specifies the format of the staged data . . . e.g.,
temporary database table, permanent database table, database
view, JDBC result set and flat file.
pass through flag The pass though flag is a Boolean flag indicating if the
associated data station operator can be ignored. This flag can
be used to turn off a data station operator without a user having
to physically remove the data station operator from a data flow.
name of staging object The name corresponds to, e.g., a staging table name or staging
file name.
data station lifetime The lifetime permits a user to specify when a staging object
should be cleaned and removed after a flow execution - i.e.,
removing a staging object at the end of an ETL flow execution,
or keeping the staging object permanently.
performance hints Performance hints include information such as DB partition
information according to the source or target tables, index
specification, whether to preserve incoming data orders etc.
Allowing a user to specify performance hints gives the user
flexibility to control data flow execution.

Advantages of a Data Station Operator

Advantages of a data station operator include the following. With respect to performance, depending on the underlying runtime engine in which an ETL process is executed, staging intermediate data can yield better performance by controlling where and how data is flown through. For example, when the underlying ETL engine is a database server (e.g., DB2), the execution code of one data flow can be represented to one or several SQL statements. One single SQL statement can contain several levels of nested sub-queries to represent many transform operations. However, one single SQL statement could lead to runtime performance problems on certain DB servers. For example, two common problems could occur which are caused by one long SQL statement: 1) the log size that is required to run the SQL can be large if the number of nested queries reaches a certain level; 2) a single (nested) query is limited to DB vendor's query processing capability. In some cases, a single SQL statement will not work. In such case it is desirable to break the single SQL statement into smaller pieces for better performance.

With regard to data (format) exchange, when a data flow includes a mix of SQL operators and non-SQL operators, it is generally not possible to represent the data flow using one common language. Data flows through must be “staged” in order to transit from one type of operator to another. For example, consider a data flow that extracts data from a JDBC (Java Database Connectivity) source, goes through a couple of transformations, and then ends with the data being loaded into a target table. The code representing JDBC extraction is a java program, whereas the transformations and loads can be presented by SQL statements. In such cases, the output row sets from the JDBC extractor are staged into a DB2 table prior to sending the row sets to the following transform node.

A data station operator also permits tracing of data within a data flow. Providing a tracing functionality in a data flow permits users to monitor and track data flow runtime execution, and helps users diagnose problems when errors occur. Providing a data station operator permits a user to explicitly specify a staging point for an operator in a data flow at which a stage table/file will be created to capture all intermediate data that have been processed up to the staging point. Additional diagnostic information for the staging point can also be captured, such as number of rows processed, code being executed, temporary tables/files created, and so on. A data station operator also provides error recovery capability for a data flow. For example, when the execution of a data flow fails, the code generation system, or user, can select to begin a recovery process from a staged point where intermediate processed data is still valid. This permits for faster recovery from a failure relative to having to restart from the beginning of a data flow.

Pre-Determined Criteria for Inserting a Data Station Operator into a Data Flow

A data exchange/staging point identities a position where data exchange/staging is required in a data flow—e.g., either on a link or an output port. In one implementation, a staging point in a data flow is identified when one of the following conditions arises:

    • An explicit exchange/staging point specified by user. A staging point can be explicitly specified by the user using a data station operator. For example, a user can specify a staging point where the user wants to examine intermediate data sets processed during runtime, which helps for debugging and diagnosis purposes when error occurs. Optionally, the user can specify the data station repository type as well.
    • An implicit exchange/staging point identified by a code generation system. There are situations where implicit staging points are required. For example, in one implementation, staging points are required for an operator in a dataflow that requires chunking—e.g., a splitter operator requires chunking if there are multiple output streams going into different targets. Custom operator can also specify if input streams and/or output streams need to be chunked. In general, operators that typically require staging include splitter operators, operators that support the discarding of rows, and custom operators that require staging. Implicit staging point may also be required for those operations of a given operator that need to be broken into multiple parts to improve performance. The following operators are example candidate operators for which a staging point may be required. Inner join operator—an inner join operator can have multiple inputs, and perform a SQL join on multiple tables. Performance of a SQL join operation depends on the underlying database query processing. It is, therefore, desirable to split one large join into multiple ones with smaller join cardinalities. In such a case, staging points are required at intermediate join stages. The type of data station can be a global temporary table for optimal performance. Key lookup operator—a key lookup operator is implemented using a SQL inner join operation and, therefore, key lookup operators can be processed similar to inner join operators.
    • Two “incompatible” operators are linked together. One operator can be incompatible with another operator when the runtime implementation code for each of the operators is different. For example, if a JDBC extract operator is implemented as a java program, it is viewed as not compatible with a join operator that is implemented using SQL. In such ease, a data station operator is placed between the two operators so the data can be passed from one type to the other.
      Example Staging Types of a Data Staging Operator

In one implementation, when a code generation system chunks a data flow into several small pieces, staging tables and staging files are created and maintained to hold intermediate row sets during an ETL process—e.g., data between extract and transform, between transform and load, or a chunking point inside a data flow. In one implementation, staging tables are database relational tables, and depending on how staging tables are used, a given stage table can be either a permanent table on ETL transform database, or a temporary table created in the data transformation session. In one implementation, staging files are flat files that hold intermediate transformed data in the text format. Staging tables and staging files can be created on a transform engine. A user can also input other specifications of a staging object, such as (table) spaces, indexes used for staging tables, location for staging files.

Staging Tables

In one implementation, staging tables are used to hold intermediate row sets during an ETL process. A code generation system can maintain a staging table, including DDL (Database Definition Language) associated table spaces and indexes. The “lifetime” of a staging table (e.g., the duration of a stage table and when should the staging table be deleted) can be externally specified by a user or internally determined by a code generation system depending on the usage of the staging object. For example, if a staging table is generated internally by a code generation system, and is used only for a specific dataflow stream, the staging table can be created at the beginning of the data flow execution as a database temporary table, which temporary table will be deleted when the session ends. If however, an internal staging table is used to chunk a data flow into multiple parallel execution pieces, the staging object can be defined as database permanent table to hold intermediate row sets until the end of an ETL application execution.

Staging Files

In one implementation, staging files are flat, text files. A flat file is a text-based ASCII file that is commonly used as a bridge between non-relational data sets and relational database tables. Staging flat files can be generated by a database export utility (such as DB2 SQL export) to export data from relational DB tables, or can be generated using a custom operator interface provided by a code generation system. Flat files can be loaded into target tables through a database load utility such as DB2 load.

JDBC Result Sets

JDBC result sets are the exchange point between two or more operators. The results of a previous (upstream) operator are represented as JDBC result sets and consumed by following (downstream) operators. JDBC result sets are memory objects and, in one implementation, the handles/names of the memory objects are determined by the code generation system.

Automatically Placed Data Station Operators

For internally generated staging points (e.g., those staging points not explicitly defined by a user), a code generation system can analyze the internal presentation of a data flow (e.g., through a QGM), identify staging points and insert data station operators that chunk the data flow into multiple smaller pieces (or sub-flows). Between these sub-flows, staging tables can be used to temporarily store intermediate transformed result sets. For example, when a chunking point is identified, a QGM can include staging tables/files (e.g., represented as table/file boxes) that link to other QGM nodes. QGM In one implementation, the name of each staging table within a QGM is unique. In one implementation, DDL statements for all staging tables generated within a data flow will be returned.

In one implementation, a code generation system (e.g., code generation system 210 of FIG. 2) chunks a QGM graph into several smaller graphs according to a chunking level (passed as an input parameter). In one implementation, a chunking level indicates the maximum number of nested queries and, accordingly, the number of QGM query boxes (e.g., select aggregate, union/except/intersect) traversed can be counted. For example, FIG. 7 illustrates a QGM graph 700 chunked into two smaller sub-graphs 702, 704, in which the chunking level is 3. When a chunking point is identified, a table box (e.g., table 706) can be added to represent the staging table, which staging table is also the top box of a chunked QGM sub-graph (e.g., sub-graph 704). The same staging box (staging table) is referred to as the starting point of a new sub-graph. The sequence of each chunked sub-graph is also tracked.

One or more of method steps described above can be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Generally, the invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In one implementation, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.

Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.

FIG. 8 illustrates a data processing system 800 suitable for storing and/or executing program code. Data processing system 800 includes a processor 802 coupled to memory elements 804A-B through a system bus 806. In other implementations, data processing system 800 may include more than one processor and each processor may be coupled directly or indirectly to one or more memory elements through a system bus.

Memory elements 804A-B can include local memory employed during actual execution of the program code, bulk storage, and cache memories that provide temporary storage of at least some program code in order to reduce the number of times the code must be retrieved from bulk storage during execution. As shown, input/output or I/O devices 808A-B (including, but not limited to, keyboards, displays, pointing devices, etc.) are coupled to data processing system 800. I/O devices 808A-B may be coupled to data processing system 800 directly or indirectly through intervening I/O controllers (not shown).

In one implementation, a network adapter 810 is coupled to data processing system 800 to enable data processing system 800 to become coupled to other data processing systems or remote printers or storage devices through communication link 812. Communication link 812 can be a private or public network. Modems, cable modems, and Ethernet cards are just a few of the currently available types of network adapters.

Various implementations for modeling data exchange in a data flow associated with an extract, transform, and load (ETL) process have been described. Nevertheless, various modifications may be made to the implementations, and those variations would be within the scope of the present invention. For example, with respect to various implementations discussed above, different programming languages (e.g., C) can be used to stage intermediate processing data into a proprietary data format. Accordingly, many modifications may be made without departing from the scope of the following claims.

Patent Citations
Cited PatentFiling datePublication dateApplicantTitle
US4813013Dec 22, 1986Mar 14, 1989The Cadware Group, Ltd.Schematic diagram generating system using library of general purpose interactively selectable graphic primitives to create special applications icons
US4901221Apr 14, 1986Feb 13, 1990National Instruments, Inc.Graphical system for modelling a process and associated method
US5379423Jun 1, 1993Jan 3, 1995Hitachi, Ltd.Information life cycle processor and information organizing method using it
US5497500Jun 6, 1995Mar 5, 1996National Instruments CorporationMethod and apparatus for more efficient function synchronization in a data flow program
US5577253Mar 6, 1995Nov 19, 1996Digital Equipment CorporationMethod executed in a computer system
US5729746Oct 23, 1995Mar 17, 1998Leonard; Ricky JackComputerized interactive tool for developing a software product that provides convergent metrics for estimating the final size of the product throughout the development process using the life-cycle model
US5850548Nov 14, 1994Dec 15, 1998Borland International, Inc.In a computer system
US5857180Jul 21, 1997Jan 5, 1999Oracle CorporationMethod and apparatus for implementing parallel operations in a database management system
US5920721Jun 11, 1997Jul 6, 1999Digital Equipment CorporationCompiler generating functionally-alike code sequences in an executable program intended for execution in different run-time environments
US5966532Aug 6, 1997Oct 12, 1999National Instruments CorporationGraphical code generation wizard for automatically creating graphical programs
US6014670Nov 7, 1997Jan 11, 2000Informatica CorporationApparatus and method for performing data transformations in data warehousing
US6044217Oct 23, 1997Mar 28, 2000International Business Machines CorporationHierarchical metadata store for an integrated development environment
US6098153Jan 30, 1998Aug 1, 2000International Business Machines CorporationMethod and a system for determining an appropriate amount of data to cache
US6202043Feb 8, 1999Mar 13, 2001Invention Machine CorporationComputer based system for imaging and analyzing a process system and indicating values of specific design changes
US6208345Jun 8, 1998Mar 27, 2001Adc Telecommunications, Inc.Visual data integration system and method
US6208990Jul 15, 1998Mar 27, 2001Informatica CorporationMethod and architecture for automated optimization of ETL throughput in data warehousing applications
US6243710Jan 21, 1999Jun 5, 2001Sun Microsystems, Inc.Methods and apparatus for efficiently splitting query execution across client and server in an object-relational mapping
US6282699Feb 23, 1999Aug 28, 2001National Instruments CorporationCode node for a graphical programming system which invokes execution of textual code
US6434739Apr 22, 1996Aug 13, 2002International Business Machines CorporationObject oriented framework mechanism for multi-target source code processing
US6449619Jun 23, 1999Sep 10, 2002Datamirror CorporationMethod and apparatus for pipelining the transformation of information between heterogeneous sets of data sources
US6480842 *Mar 25, 1999Nov 12, 2002Sap Portals, Inc.Dimension to domain server
US6604110 *Oct 31, 2000Aug 5, 2003Ascential Software, Inc.Automated software code generation from a metadata-based repository
US6738964Mar 10, 2000May 18, 2004Texas Instruments IncorporatedGraphical development system and method
US6772409 *Mar 2, 1999Aug 3, 2004Acta Technologies, Inc.Specification to ABAP code converter
US6795790Jun 6, 2002Sep 21, 2004Unisys CorporationMethod and system for generating sets of parameter values for test scenarios
US6807651Jun 17, 2002Oct 19, 2004Cadence Design Systems, Inc.Procedure for optimizing mergeability and datapath widths of data flow graphs
US6839724 *Apr 17, 2003Jan 4, 2005Oracle International CorporationMetamodel-based metadata change management
US6850925May 15, 2001Feb 1, 2005Microsoft CorporationQuery optimization by sub-plan memoization
US6968326Jul 17, 2003Nov 22, 2005Vivecon CorporationSystem and method for representing and incorporating available information into uncertainty-based forecasts
US6968335 *Nov 14, 2002Nov 22, 2005Sesint, Inc.Method and system for parallel processing of database queries
US6978270 *Nov 16, 2001Dec 20, 2005Ncr CorporationSystem and method for capturing and storing operational data concerning an internet service provider's (ISP) operational environment and customer web browsing habits
US7003560Nov 3, 2000Feb 21, 2006Accenture LlpData warehouse computing system
US7031987 *May 30, 2001Apr 18, 2006Oracle International CorporationIntegrating tablespaces with different block sizes
US7035786Oct 26, 2001Apr 25, 2006Abu El Ata Nabil ASystem and method for multi-phase system development with predictive modeling
US7076765Mar 3, 1999Jul 11, 2006Kabushiki Kaisha ToshibaSystem for hiding runtime environment dependent part
US7103590 *Aug 24, 2001Sep 5, 2006Oracle International CorporationMethod and system for pipelined database table functions
US7191183 *Apr 5, 2002Mar 13, 2007Rgi Informatics, LlcAnalytics and data warehousing infrastructure and services
US7209925 *Aug 25, 2003Apr 24, 2007International Business Machines CorporationMethod, system, and article of manufacture for parallel processing and serial loading of hierarchical data
US7340718May 8, 2003Mar 4, 2008Sap AgUnified rendering
US7343585Jan 29, 2003Mar 11, 2008Oracle International CorporationOperator approach for generic dataflow designs
US7499917Jan 28, 2005Mar 3, 2009International Business Machines CorporationProcessing cross-table non-Boolean term conditions in database queries
US7689576Mar 10, 2006Mar 30, 2010International Business Machines CorporationDilation of sub-flow operators in a data flow
US7689582Mar 10, 2006Mar 30, 2010International Business Machines CorporationData flow system and method for heterogeneous data integration environments
US7739267Mar 10, 2006Jun 15, 2010International Business Machines CorporationClassification and sequencing of mixed data flows
US7747563 *Dec 7, 2007Jun 29, 2010Breakaway Technologies, Inc.System and method of data movement between a data source and a destination
US20020046301 *Aug 13, 2001Apr 18, 2002Manugistics, Inc.System and method for integrating disparate networks for use in electronic communication and commerce
US20020078262Dec 14, 2000Jun 20, 2002Curl CorporationSystem and methods for providing compatibility across multiple versions of a software system
US20020116376Feb 28, 2002Aug 22, 2002Hitachi, Ltd.Routine executing method in database system
US20020170035Feb 28, 2001Nov 14, 2002Fabio CasatiEvent-based scheduling method and system for workflow activities
US20020198872Feb 4, 2002Dec 26, 2002Sybase, Inc.Database system providing optimization of group by operator over a union all
US20030033437Apr 9, 2002Feb 13, 2003Fischer Jeffrey MichaelMethod and system for using integration objects with enterprise business applications
US20030037322Jun 21, 2002Feb 20, 2003Kodosky Jeffrey L.Graphically configuring program invocation relationships by creating or modifying links among program icons in a configuration diagram
US20030051226Jun 13, 2001Mar 13, 2003Adam ZimmerSystem and method for multiple level architecture by use of abstract application notation
US20030101098Nov 27, 2001May 29, 2003Erich SchaarschmidtProcess and device for managing automatic data flow between data processing units for operational order processing
US20030110470May 31, 2002Jun 12, 2003Microsoft CorporationMethod and apparatus for providing dynamically scoped variables within a statically scoped computer programming language
US20030149556Feb 21, 2001Aug 7, 2003Riess Hugo ChristianMethod for modelling and controlling real processes in a data processing equipment and a data processing equipment for carrying out said method
US20030154274Jan 31, 2003Aug 14, 2003International Business Machines CorporationData communications system, terminal, and program
US20030172059Oct 30, 2002Sep 11, 2003Sybase, Inc.Database system providing methodology for eager and opportunistic property enforcement
US20030182651Mar 21, 2002Sep 25, 2003Mark SecristMethod of integrating software components into an integrated solution
US20030229639Jun 7, 2002Dec 11, 2003International Business Machines CorporationRuntime query optimization for dynamically selecting from multiple plans in a query based upon runtime-evaluated performance criterion
US20030233374Dec 2, 2002Dec 18, 2003Ulrich SpinolaDynamic workflow process
US20030236788Jun 3, 2002Dec 25, 2003Nick KanellosLife-cycle management engine
US20040054684Nov 12, 2001Mar 18, 2004Kay GeelsMethod and system for determining sample preparation parameters
US20040068479 *Oct 4, 2002Apr 8, 2004International Business Machines CorporationExploiting asynchronous access to database operations
US20040107414Oct 1, 2003Jun 3, 2004Youval BronickiMethod, a language and a system for the definition and implementation of software solutions
US20040220923Apr 28, 2004Nov 4, 2004Sybase, Inc.System and methodology for cost-based subquery optimization using a left-deep tree join enumeration algorithm
US20040254948 *Jun 12, 2003Dec 16, 2004International Business Machines CorporationSystem and method for data ETL in a data warehouse environment
US20050022157Dec 23, 2003Jan 27, 2005Rainer BrendleApplication management
US20050044527Aug 22, 2003Feb 24, 2005Gerardo RecintoCode Units based Framework for domain- independent Visual Design and Development
US20050055257 *Sep 4, 2003Mar 10, 2005Deniz SenturkTechniques for performing business analysis based on incomplete and/or stage-based data
US20050091664Sep 10, 2004Apr 28, 2005Jay CookMethod and system for associating parameters of containers and contained objects
US20050091684Sep 22, 2004Apr 28, 2005Shunichi KawabataRobot apparatus for supporting user's actions
US20050097103 *Sep 17, 2004May 5, 2005Netezza CorporationPerforming sequence analysis as a multipart plan storing intermediate results as a relation
US20050108209Nov 19, 2003May 19, 2005International Business Machines CorporationContext quantifier transformation in XML query rewrite
US20050131881Sep 16, 2004Jun 16, 2005Bhaskar GhoshExecuting a parallel single cursor model
US20050137852Jan 8, 2004Jun 23, 2005International Business Machines CorporationIntegrated visual and language- based system and method for reusable data transformations
US20050149914Oct 29, 2004Jul 7, 2005Codemesh, Inc.Method of and system for sharing components between programming languages
US20050174986Feb 11, 2004Aug 11, 2005Radio Ip Software, Inc.Method and system for emulating a wirless network
US20050174988Dec 30, 2004Aug 11, 2005Bernt BieberMethod and arrangement for controlling access to sensitive data stored in an apparatus, by another apparatus
US20050188353Feb 20, 2004Aug 25, 2005International Business Machines CorporationMethod and system for retaining formal data model descriptions between server-side and browser-side javascript objects
US20050216497Mar 26, 2004Sep 29, 2005Microsoft CorporationUniform financial reporting system interface utilizing staging tables having a standardized structure
US20050227216Nov 19, 2004Oct 13, 2005Gupta Puneet KMethod and system for providing access to electronic learning and social interaction within a single application
US20050234969Feb 24, 2005Oct 20, 2005Ascential Software CorporationServices oriented architecture for handling metadata in a data integration platform
US20050240354 *Feb 24, 2005Oct 27, 2005Ascential Software CorporationService oriented architecture for an extract function in a data integration platform
US20050240652Mar 8, 2005Oct 27, 2005International Business Machines CorporationApplication Cache Pre-Loading
US20050243604 *Mar 16, 2005Nov 3, 2005Ascential Software CorporationMigrating integration processes among data integration platforms
US20050256892 *Mar 16, 2005Nov 17, 2005Ascential Software CorporationRegenerating data integration functions for transfer from a data integration platform
US20050283473Sep 21, 2004Dec 22, 2005Armand RoussoApparatus, method and system of artificial intelligence for data searching applications
US20060004863Jun 8, 2004Jan 5, 2006International Business Machines CorporationMethod, system and program for simplifying data flow in a statement with sequenced subexpressions
US20060015380Jun 15, 2005Jan 19, 2006Manyworlds, IncMethod for business lifecycle management
US20060036522Sep 23, 2004Feb 16, 2006Michael PerhamSystem and method for a SEF parser and EDI parser generator
US20060047709Sep 5, 2002Mar 2, 2006Belin Sven JTechnology independent information management
US20060074621Aug 31, 2004Apr 6, 2006Ophir RachmanApparatus and method for prioritized grouping of data representing events
US20060074730Jan 31, 2005Apr 6, 2006Microsoft CorporationExtensible framework for designing workflows
US20060101011Nov 5, 2004May 11, 2006International Business Machines CorporationMethod, system and program for executing a query having a union operator
US20060112109 *Nov 23, 2004May 25, 2006Chowdhary Pawan RAdaptive data warehouse meta model
US20060167865Jan 24, 2005Jul 27, 2006Sybase, Inc.Database System with Methodology for Generating Bushy Nested Loop Join Trees
US20060174225Feb 1, 2005Aug 3, 2006International Business Machines CorporationDebugging a High Level Language Program Operating Through a Runtime Engine
US20060206869Apr 21, 2006Sep 14, 2006Lewis Brad RMethods and systems for developing data flow programs
US20060212475 *Nov 24, 2003Sep 21, 2006Cheng Nick TEnterprise information management and business application automation by using the AIMS informationbase architecture
US20060218123Jun 2, 2005Sep 28, 2006Sybase, Inc.System and Methodology for Parallel Query Optimization Using Semantic-Based Partitioning
US20060228654Apr 7, 2005Oct 12, 2006International Business Machines CorporationSolution builder wizard
US20070061305 *Apr 27, 2006Mar 15, 2007Soufiane AziziSystem and method of providing date, arithmetic and other relational functions for OLAP sources
US20070078812Sep 30, 2005Apr 5, 2007Oracle International CorporationDelaying evaluation of expensive expressions in a query
US20070157191Dec 29, 2005Jul 5, 2007Seeger Frank ELate and dynamic binding of pattern components
US20070169040Jan 13, 2006Jul 19, 2007Microsoft CorporationTyped intermediate language support for languages with multiple inheritance
US20070203893 *Feb 27, 2006Aug 30, 2007Business Objects, S.A.Apparatus and method for federated querying of unstructured data
US20070214111Mar 10, 2006Sep 13, 2007International Business Machines CorporationSystem and method for generating code for an integrated data system
US20070214171 *Mar 10, 2006Sep 13, 2007International Business Machines CorporationData flow system and method for heterogeneous data integration environments
US20070214176Mar 10, 2006Sep 13, 2007International Business Machines CorporationDilation of sub-flow operators in a data flow
US20070244876Mar 10, 2006Oct 18, 2007International Business Machines CorporationData flow system and method for heterogeneous data integration environments
US20080092112Oct 11, 2006Apr 17, 2008International Business Machines CorporationMethod and Apparatus for Generating Code for an Extract, Transform, and Load (ETL) Data Flow
US20080147703Oct 11, 2006Jun 19, 2008International Business Machines CorporationMethod and Apparatus for Managing Application Parameters
US20080147707Dec 13, 2006Jun 19, 2008International Business Machines CorporationMethod and apparatus for using set based structured query language (sql) to implement extract, transform, and load (etl) splitter operation
Non-Patent Citations
Reference
1 *Alkis Simitisis, Mapping Conceptual to Logical Models for ETL Processes, ACM, 2005.
2 *Alkis Simitsis. "Mapping Conceptual to Logical Models for ETL Processes." ACM, 2005, pp. 67-77.
3Arusinski et al., "A Software Port from a Standalone Communications Management Unit to an Integrated Platform", 2002, IEEE, pp. 1-9.
4Carreira et al., "Data Mapper: An Operator for Expressing One-to Many Data Transformations", Data Warehousing and Knowledge Discovery, Tjoa et al, editors, 7th International Conference DaWaK 2005 Copenhagen, Denmark, Aug. 22-26, 2005, pp. 136-145.
5Carreira et al., "Execution of Data Mappers", IQIS, 2004, pp. 2-9, 2004 ACM 1-58113-902-0/04/0006, Paris, France.
6Ferguson et al., "Platform Independent Translations for a Compilable Ada Abstract Syntax", Feb. 1993 ACM 0-89791-621-2/93/0009-0312 1.50, pp. 312-322.
7Final Office Action for U.S. Appl. No. 11/610,480 dated Apr. 13, 2011.
8Friedrich, II, Meta-Data Version and Configuration Management in Multi-Vendor Environments, SIGMOD, Jun. 14-16, 2005, 6 pgs., Baltimore, MD.
9Gurd et al., "The Manchester Prototype Dataflow Computer", Communications of the ACM, Jan. 1985, pp. 34-52, vol. 28, No. 1.
10Haas et al., "Clio Grows Up: From Research Prototype to Industrial Tool", SIGMOD, Jun. 14-16, 2005, 6 pgs., Baltimore, MD.
11Hernandez et al., "Clio: A schema mapping tool for information integration", IEEE Computer Society, 2005.
12Ives, Zachary G., An Adaptive Query Execution System for Data Integration, Proceedings of the 1999 ACM SIGMOD international conference on Management of data, Jun. 1999, vol. 28, Issue 2, ACM, New York, New York, United States.
13Jardim-Gonçalves et al., "Integration and adoptability of APs: the role of ISO TC184/SC4 standards", International Journal of Computer Applications in Technology, 2003, pp. 105-116, vol. 18, Nos. 1-4.
14Konstantinides, et al., "The Khoros Software Development Environment for Image and Signal Processing," May 1994, IEEE, vol. 3, pp. 243-252.
15Notice of Allowance for U.S. Appl. No. 11/548,659 dated May 13, 2011.
16Office Action for U.S. Appl. No. 11/372,540 dated Mar. 30, 2011.
17Office Action history of U.S. Appl. No. 11/372,516, dates ranging from Apr. 6, 2006 to Nov. 17, 2009.
18Office Action history of U.S. Appl. No. 11/372,540, dates ranging from Mar. 11, 2009 to Sep. 19, 2011.
19Office Action history of U.S. Appl. No. 11/373,084, dates ranging from Feb. 20, 2009 to Feb. 3, 2010.
20Office Action history of U.S. Appl. No. 11/373,685, dates ranging from Jan. 10, 2008 to Nov. 16, 2009.
21Office Action history of U.S. Appl. No. 11/548,632, dates ranging from May 11, 2010 to Jul. 11, 2011.
22Office Action history of U.S. Appl. No. 11/548,659, dates ranging from Nov. 10, 2010 to Sep. 14, 201t.
23Office Action history of U.S. Appl. No. 11/610,480, dates ranging from Sep. 10, 2010 to Aug. 31, 2011.
24Poess et al., "TPC-DS, Taking Decision Support Benchmarking to the Next Level", ACM SIGMOD, Jun. 4-6, 2002, 6 pgs., Madison, WI.
25Rafaieh et al., "Query-based data warehousing tool", DOLAP, Nov. 8, 2002, 8 pgs., McLean, VA.
26Ramu, "Method for Initializing a Plateform and Code Independent Library", IBM Technical Disclosure Bulletin, Sep. 1994, pp. 637-638, vol. 37, No. 9.
27Simitsis, "Mapping Conceptual to Logical Models for ETL Processes", ACM Digital Library, 2005, pp. 67-76.
28Stewart et al., "Dynamic Applications from the Ground Up", Haskell '05, Sep. 30, 2005, Tallinn, Estonia, ACM, pp. 27-38.
29Tjoa, et al. (Eds.), "Data Warehousing and Knowledge Discovery," Proceedings of 7th International Conference, DaWaK 2005, Copenhagen, Denmark, Aug. 22-26, 2005, Springer 2005.
30U.S. Appl. No. 09/707,504, filed Nov. 7, 2000, Banavar, et al.
31U.S. Patent Application entitled "Method and Apparatus for Adapting Application Front-Ends to Execute on Heterogeneous Device Platforms", filed Nov. 7, 2000.
32Vassiliadis et al., "A generic and customizable framework for the design of ETL scenarios", Information Systems, Databases: Creation, Management and Utilization, 2005, pp. 492-525, vol. 30, No. 7.
33Werner et al., "Just-in-sequence material supply-a simulation based solution in electronics", Robotics and Computer-Integrated Manufacturing, 2003, pp. 107-111, vol. 19, Nos. 1-2.
34Werner et al., "Just-in-sequence material supply—a simulation based solution in electronics", Robotics and Computer-Integrated Manufacturing, 2003, pp. 107-111, vol. 19, Nos. 1-2.
35Yu, "Transform Merging of ETL Data Flow Plan", IKE '03 International Conference, 2003, pp. 193-198.
36Zhao et al., "Automated Glue/Wrapper Code Generation in Integration of Distributed and Heterogeneous Software Components", Proceedings of the 8th IEEE International Enterprise Distributed Object Computing Conf. (EDOC 2004), 2004, IEEE, pp. 1-11.
Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US8538912 *Sep 22, 2010Sep 17, 2013Hewlett-Packard Development Company, L.P.Apparatus and method for an automatic information integration flow optimizer
US8751438 *Apr 13, 2012Jun 10, 2014Verizon Patent And Licensing Inc.Data extraction, transformation, and loading
US8782101 *Jan 20, 2012Jul 15, 2014Google Inc.Transferring data across different database platforms
US8812482Oct 14, 2010Aug 19, 2014Vikas KapoorApparatuses, methods and systems for a data translator
US20120072391 *Sep 22, 2010Mar 22, 2012Alkiviadis SimitsisApparatus and method for an automatic information integration flow optimizer
US20130275360 *Apr 13, 2012Oct 17, 2013Verizon Patent And Licensing Inc.Data extraction, transformation, and loading
Classifications
U.S. Classification707/602, 707/798
International ClassificationG06F7/00
Cooperative ClassificationG06F8/20
European ClassificationG06F8/20
Legal Events
DateCodeEventDescription
Jan 23, 2007ASAssignment
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, CALIF
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:JIN, QI;LIAO, HUI;SRINIVASAN, SRIRAM;AND OTHERS;REEL/FRAME:018793/0408
Effective date: 20070105